geomesa

If you really need to scale beyond what Postgres/PostGIS can handle, then you might want to check out GeoMesa[1], which is (very loosely) "PostGIS for HBase, Cassandra, or Google BigTable". That being said, you may not need it, because Postgres/PostGIS can scale vertically to handle larger datasets than most people realize. I recommend loading your intended data (or your best simulation of it) into a Postgres instance running on one of the extremely large VMs available on your cloud provider, and running a load test with a distribution of the queries you'd expect. Assuming the deliberately over-provisioned instance is able to handle the queries, you can then run some experiments to "right-size" the instance to find the right balance of compute, memory, SSD, etc. If it can handle the queries but not at the QPS you need, then read replicas

If you're at the limits of "scaling up" in PostGIS you could look into "scaling out" by migrating to a NoSQL solution like Cassandra or HBase + GeoMesa[1]. However, this would be a pretty significant change and introduces a lot of new operational burden, so I wouldn't do it unless you think you can't meet your goals with PostGIS. I think a good first step would be bringing in a PostGIS consultant who can give you some expert advice on tuning for your current workload. [1] https://github.com/locationtech/geomesa

If you outgrow PostGIS, you may want to look into GeoMesa, which allows you to set up spatio-temporal indexing on top of NoSQL column stores including HBase and Cassandra: https://github.com/locationtech/geomesa

Here's an implementation of the algorithm in Scala: https://github.com/locationtech/geomesa/blob/master/geomesa-...